Papers by Yuting Huang
Pun2Pun: Benchmarking LLMs on Textual-Visual Chinese-English Pun Translation via Pragmatics Model and Linguistic Reasoning (2025.acl-srw)
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| Challenge: | Current approaches resort to suboptimal compromises and computational methods remain inadequate for translation. |
| Approach: | They propose a Constant-Variable Optimization (CVO) model for translation strategy and an Ovl metric for translation quality assessment that adapts to Chinese and English. |
| Outcome: | The proposed model improves performance on textual and visual puns while maintaining linguistic mechanisms and humorous effects. |
Rewrite to Jailbreak: Discover Learnable and Transferable Implicit Harmfulness Instruction (2025.findings-acl)
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| Challenge: | Existing jailbreak methods create a forced instruction-following scenario, or search adversarial prompts with prefix or suffix tokens to achieve a specific representation manually or automatically. |
| Approach: | They propose a method that rewrites the original instruction to achieve a jailbreak . they propose rewriting the original instructions to improve the attack strategy . |
| Outcome: | The proposed method is more efficient and easier to identify since no additional features are introduced. |
PAD: A Robustness Enhancement Ensemble Method via Promoting Attention Diversity (2024.lrec-main)
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| Challenge: | Existing approaches to enhance robustness of deep neural networks focus on perturbation . weak robustness is a problem for many types of adversarial attacks, authors say . |
| Approach: | They propose a lightweight framework for enhancing robustness by perturbing parameters of a model and diversifying adversarial example distributions among different models. |
| Outcome: | The proposed method can improve robustness against adversarial attacks while maintaining accuracy on clean data. |
S2-MAD: Breaking the Token Barrier to Enhance Multi-Agent Debate Efficiency (2025.naacl-long)
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Yuting Zeng, Weizhe Huang, Lei Jiang, Tongxuan Liu, XiTai Jin, Chen Tianying Tiana, Jing Li, Xiaohua Xu
| Challenge: | Large language models exhibit limitations when handling complex mathematical reasoning and logical inference tasks. |
| Approach: | They propose a sparsification strategy to reduce token costs within Multi-agent Debate (MAD) this strategy minimizes ineffective exchanges of information and unproductive discussions among agents . |
| Outcome: | The proposed approach reduces token costs by up to 94.5% while maintaining performance degradation below 2.0%. |
SplitThenMerge: Token-Level Skill-Compositional Sparse Mixture-of-Experts for Complex Domain-Specific Tasks (2026.findings-acl)
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| Challenge: | Existing domain adaptation methods train heterogeneous skills together, making it difficult to reliably coordinate multiple skills when solving complex tasks. |
| Approach: | They propose a framework that decomposes domain competence into atomic skills and composes them dynamically during generation. |
| Outcome: | The proposed framework decomposes domain competence into atomic skills, trains them independently, and composes them dynamically during generation. |
"I Don’t Know What to Say": A Fact-Filling Questionnaire Method to Help Non-Experts Talk to LegalAI Assistant (2026.findings-acl)
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| Challenge: | Recent advances in large language models (LLMs) have greatly expanded the scope of legal AI. |
| Approach: | They propose a method that generates questionnaires to help users refine queries . they leverage an iterative training process that collects valuable questionnaires . |
| Outcome: | The proposed method improves the completeness of queries and ensures the performance of domain-specific models in downstream legal tasks. |
Logic-of-Thought: Injecting Logic into Contexts for Full Reasoning in Large Language Models (2025.naacl-long)
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Tongxuan Liu, Wenjiang Xu, Weizhe Huang, Yuting Zeng, Jiaxing Wang, Xingyu Wang, Hailong Yang, Jing Li
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks but their performance in complex logical reasoning tasks remains unsatisfactory. |
| Approach: | They propose a propositional logic prompting method which generates expanded logical information descriptions and utilizes them as an additional augmentation to original contexts. |
| Outcome: | Extensive experiments show that Logic-of-Thought boosts the performance of various prompting methods with a striking margin across five logical reasoning tasks. |